scholarly journals Contour Extraction Based on Adaptive Thresholding in Sonar Images

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 354
Author(s):  
Antonios Andreatos ◽  
Apostolos Leros
Keyword(s):  
Large Scale ◽  
Extraction Methods ◽  
Automatic Target Recognition ◽  
Multimedia Databases ◽  
Contour Extraction ◽  
Data Smoothing ◽  
Side Scan Sonar ◽  
Binary Images ◽  
Polynomial Curve ◽  
Sonar Images

A common problem in underwater side-scan sonar images is the acoustic shadow generated by the beam. Apart from that, there are a number of reasons impairing image quality. In this paper, an innovative algorithm with two alternative histogram approximation methods is presented. Histogram approximation is based on automatically estimating the optimal threshold for converting the original gray scale images into binary images. The proposed algorithm clears the shadows and masks most of the impairments in side-scan sonar images. The idea is to select a proper threshold towards the rightmost local minimum of the histogram, i.e., closest to the white values. For this purpose, the histogram envelope is approximated by two alternative contour extraction methods: polynomial curve fitting and data smoothing. Experimental results indicate that the proposed algorithm produces superior results than popular thresholding methods and common edge detection filters, even after corrosion expansion. The algorithm is simple, robust and adaptive and can be used in automatic target recognition, classification and storage in large-scale multimedia databases.

scholarly journals Real-Time Underwater Maritime Object Detection in Side-Scan Sonar Images Based on Transformer-YOLOv5

2021 ◽  
Vol 13 (18) ◽  
pp. 3555
Author(s):  
Yongcan Yu ◽  
Jianhu Zhao ◽  
Quanhua Gong ◽  
Chao Huang ◽  
Gen Zheng ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Target Localization ◽  
Average Precision ◽  
Image Preprocessing ◽  
Side Scan Sonar ◽  
Sonar Images ◽  
Underwater Target

To overcome the shortcomings of the traditional manual detection of underwater targets in side-scan sonar (SSS) images, a real-time automatic target recognition (ATR) method is proposed in this paper. This method consists of image preprocessing, sampling, ATR by integration of the transformer module and YOLOv5s (that is, TR–YOLOv5s), and target localization. By considering the target-sparse and feature-barren characteristics of SSS images, a novel TR–YOLOv5s network and a down-sampling principle are put forward, and the attention mechanism is introduced in the method to meet the requirements of accuracy and efficiency for underwater target recognition. Experiments verified the proposed method achieved 85.6% mean average precision (mAP) and 87.8% macro-F2 score, and brought 12.5% and 10.6% gains compared with the YOLOv5s network trained from scratch, and had the real-time recognition speed of about 0.068 s per image.

scholarly journals Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 29416-29428
Author(s):  
Xiaoming Qin ◽  
Xiaowen Luo ◽  
Ziyin Wu ◽  
Jihong Shang
Keyword(s):  
Deep Learning ◽  
Side Scan Sonar ◽  
Sediment Classification ◽  
Sonar Images ◽  
Small Side

Deblurring of GLORIA side-scan sonar images

1992 ◽  
Vol 14 (2) ◽  
pp. 125-136 ◽  
Author(s):  
D. C. Mason ◽  
T. P. LeBas ◽  
I. Sewell ◽  
C. Angelikaki
Keyword(s):  
Side Scan Sonar ◽  
Sonar Images

scholarly journals A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex

2019 ◽  
Vol 11 (11) ◽  
pp. 1281 ◽  
Author(s):  
Xiufen Ye ◽  
Haibo Yang ◽  
Chuanlong Li ◽  
Yunpeng Jia ◽  
Peng Li
Keyword(s):  
Constant Coefficient ◽  
Correction Method ◽  
Time Varying ◽  
Gray Level ◽  
Original Image ◽  
Gray Scale ◽  
Side Scan Sonar ◽  
Retinex Theory ◽  
Sonar Images ◽  
Beam Patterns

When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.

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